Improving plant biomass estimation in the field using partial least squares regression and ridge regression
| Autor: | John N. Klironomos, Miranda M. Hart, Kari E. Dunfield, Brian M. Ohsowski |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0106 biological sciences
Estimation geography geography.geographical_feature_category Ecology fungi food and beverages Biomass Plant Science Destructive sampling Biology 010603 evolutionary biology 01 natural sciences Regression Field (geography) Ridge Botany Partial least squares regression Ecology Evolution Behavior and Systematics Primary productivity 010606 plant biology & botany |
| Zdroj: | Botany. 94:501-508 |
| ISSN: | 1916-2804 1916-2790 |
| DOI: | 10.1139/cjb-2016-0009 |
| Popis: | Estimating primary productivity over time is challenging for plant ecologists. The most accurate biomass measurements require destructive sampling and weighing. This is often not possible for manipulative studies that involve repeated measures over time, or for studies in protected areas. Estimates of aboveground plant biomass using allometric equations or linear regression on single plant traits have been used, but tend to have poor predictability both within and across systems, or are limited to specific plant taxa. Here we estimate aboveground plant biomass using multiple collinear plant traits to generate a standard curve specific to the site of interest. Partial least squares regression (PLS) and ridge regression (RR), addressing predictor collinearity, are robust, highly accurate statistical methods to estimate plant biomass across gross differences in plant morphology and growth habit. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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